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Get Started Free →Web scraping with structured data extraction - define your output schema
.claude/skills/gooseworks-ai-structured-scraping-riveter/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 31% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 59% | 0% |
| case-06 | ✗→✓ | ▲ Improved | -14% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 42% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 22% | 0% |
Read your credentials from ~/.gooseworks/credentials.json:
bashexport GOOSEWORKS_API_KEY=$(python3 -c "import json;print(json.load(open('$HOME/.gooseworks/credentials.json'))['api_key'])") export GOOSEWORKS_API_BASE=$(python3 -c "import json;print(json.load(open('$HOME/.gooseworks/credentials.json')).get('api_base','https://api.gooseworks.ai'))")
If ~/.gooseworks/credentials.json does not exist, tell the user to run: npx gooseworks login
All endpoints use Bearer auth: -H "Authorization: Bearer $GOOSEWORKS_API_KEY"
Scrape web pages and extract data into your defined structure.
Scrape a webpage and return the text content. This endpoint allows you to extract text content from any public webpage.
Parameters:
bashcurl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \ -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \ -H "Content-Type: application/json" \ -d '{"api":"riveter","path":"/v1/scrape","body":{"url":"https://example.com/article"}}'
Copy link Define the structure of your output directly in the API request. This endpoint allows you to define both your input data and output configuration in a single request.
Parameters:
bashcurl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \ -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \ -H "Content-Type: application/json" \ -d '{"api":"riveter","path":"/v1/run"}' "input": { "urls": ["https://example.com/products"] }, "output": { "name": {"prompt": "Product name", "contexts": ["urls"]}, "price": {"prompt": "Product price", "contexts": ["urls"], "format": "number"} } }'
Retrieve the processed data from a completed project run
Parameters:
bashcurl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \ -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \ -H "Content-Type: application/json" \ -d '{"api":"riveter","path":"/v1/run_data","query":{"run_key":"abc123"}}'
Check the current status of a project run
Parameters:
bashcurl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \ -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \ -H "Content-Type: application/json" \ -d '{"api":"riveter","path":"/v1/run_status","query":{"run_key":"abc123"}}'
Stop a currently running project. This will halt all processing and mark the run as stopped. Behavior: If the run is already stopped or success, returns success with current status. If the run is in progress, stops all pending cells and marks the run as stopped. Stopped runs cannot be resumed
Parameters:
bashcurl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \ -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \ -H "Content-Type: application/json" \ -d '{"api":"riveter","path":"/v1/stop_run","query":{"run_key":"abc123"}}'
For full endpoint details and parameters:
bashcurl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/search \ -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \ -H "Content-Type: application/json" \ -d '{"prompt":"riveter API endpoints"}' List all endpoints curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/details \ -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \ -H "Content-Type: application/json" \ -d '{"api":"riveter","path":"/v1/scrape"}' # Get endpoint details
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 10,773 | 7,436 | -31% | 1 | 1 | 0% | 2,454 | 1,930 | -21% | 0 | 0 | — |
case-02 | fail→fail | 4,689 | 9,133 | +95% | 1 | 1 | 0% | 842 | 2,121 | +152% | 0 | 0 | — |
case-03 | fail→fail | 4,333 | 7,557 | +74% | 1 | 1 | 0% | 756 | 2,121 | +181% | 0 | 0 | — |
case-04 | fail→pass | 8,995 | 5,197 | -42% | 1 | 1 | 0% | 2,088 | 2,741 | +31% | 0 | 0 | — |
case-05 | fail→pass | 6,310 | 1,309 | -79% | 1 | 1 | 0% | 1,171 | 1,865 | +59% | 0 | 0 | — |
case-06 | fail→pass | 13,703 | 3,681 | -73% | 1 | 1 | 0% | 2,861 | 2,467 | -14% | 0 | 0 | — |
case-07 | fail→pass | 10,463 | 6,270 | -40% | 1 | 1 | 0% | 1,892 | 2,690 | +42% | 0 | 0 | — |
case-08 | fail→pass | 11,663 | 4,493 | -61% | 1 | 1 | 0% | 2,066 | 2,515 | +22% | 0 | 0 | — |
case-09 | fail→pass | 7,873 | 2,557 | -68% | 1 | 1 | 0% | 1,499 | 2,083 | +39% | 0 | 0 | — |
case-10 | pass→pass | 8,628 | 2,493 | -71% | 1 | 1 | 0% | 1,564 | 2,119 | +35% | 0 | 0 | — |
case-11 | fail→pass | 9,970 | 2,908 | -71% | 1 | 1 | 0% | 1,715 | 2,075 | +21% | 0 | 0 | — |
case-12 | fail→pass | 14,808 | 4,393 | -70% | 1 | 1 | 0% | 2,669 | 2,477 | -7% | 0 | 0 | — |
case-13 | fail→pass | 10,163 | 2,882 | -72% | 1 | 1 | 0% | 1,872 | 2,213 | +18% | 0 | 0 | — |
case-14 | fail→pass | 17,362 | 3,014 | -83% | 1 | 1 | 0% | 1,129 | 2,219 | +97% | 0 | 0 | — |
case-15 | fail→pass | 10,700 | 4,017 | -62% | 1 | 1 | 0% | 2,060 | 2,478 | +20% | 0 | 0 | — |
case-16 | fail→pass | 4,846 | 2,053 | -58% | 1 | 1 | 0% | 890 | 2,106 | +137% | 0 | 0 | — |
case-17 | fail→pass | 10,531 | 2,818 | -73% | 1 | 1 | 0% | 1,980 | 2,265 | +14% | 0 | 0 | — |
case-18 | fail→pass | 8,925 | 2,325 | -74% | 1 | 1 | 0% | 1,406 | 2,016 | +43% | 0 | 0 | — |
case-19 | fail→pass | 7,562 | 3,029 | -60% | 1 | 1 | 0% | 1,392 | 2,177 | +56% | 0 | 0 | — |
case-20 | fail→pass | 5,876 | 2,708 | -54% | 1 | 1 | 0% | 1,125 | 2,187 | +94% | 0 | 0 | — |
case-21 | pass→pass | 6,604 | 4,201 | -36% | 1 | 1 | 0% | 1,277 | 2,424 | +90% | 0 | 0 | — |
case-22 | pass→pass | 6,606 | 5,240 | -21% | 1 | 1 | 0% | 1,147 | 2,821 | +146% | 0 | 0 | — |
case-23 | pass→pass | 5,978 | 5,076 | -15% | 1 | 1 | 0% | 1,251 | 2,476 | +98% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted, and 19 counted toward the lift figure. The other 4 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +70 percentage points is the difference between those two pass rates over the 19 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.